27. Monitoring of Agricultural Expansion Using Hybrid Classification Method in Southwestern Fringes of Wadi El-Natrun, Egypt: An Appraisal for Sustainable Development

Agricultural development activities are essential for sustainable development in arid and semiarid regions. Therefore, a hybrid image classification methodology was proposed to monitor agricultural expansion and identify its linkages to sustainable development goals (SDGs). It was applied in the southwestern fringes of Wadi El-Natrun, Egypt, which is considered a promising area for reclamation projects via groundwater. Based on the inspection of the existing land cover (LC) types, spectral indices, i.e., normalized difference vegetation index (NDVI), bare soil index (BSI), and dry built-up index (DBI), were used to get training samples for the obtained Landsat images. Then, the maximum likelihood algorithm followed by a change detection procedure was applied to delineate LC trends between 2007 and 2022. The resulting LC maps exhibited high accuracy, with a Kappa coefficient equal to ~0.90. In addition, the agricultural regions grew from nearly 7.64% of the whole area to about 43.72%, accompanied by a severe shortage in the desert land of roughly 36.30%. However, the urban areas witnessed a small gain of approximately 0.23%. It was observed that barren land was the major contributor to new cultivation land by ~37.42%. The study also demonstrated that the significant encroachment of arable land might have reinforcing linkages to SDG targets focusing on ending desertification, poverty, and hunger. However, conflicting linkages existed with SDG targets concerning affordable drinking water and aquifer protection from water deterioration. Consequently, decision-makers should adopt management policies like the utilization of greenhouse farming systems to mitigate the negative consequences of LC alteration.